Background: Tagged Magnetic Resonance (tMR) imaging is a powerful technique for determining cardiovascular abnormalities. One of the reasons for tMR not being used in routine clinical practice is the lack of easy-to-use tools for image analysis and strain mapping. In this paper, we introduce a novel interdisciplinary method based on correlation image velocimetry (CIV) to estimate cardiac deformation and strain maps from tMR images.

Methods: CIV, a cross-correlation based pattern matching algorithm, analyses a pair of images to obtain the displacement field at sub-pixel accuracy with any desired spatial resolution. This first time application of CIV to tMR image analysis is implemented using an existing open source Matlab-based software called UVMAT. The method, which requires two main input parameters namely correlation box size (CB) and search box size (SB), is first validated using a synthetic grid image with grid sizes representative of typical tMR images. Phantom and patient images obtained from a Medical Imaging grand challenge dataset (http://stacom.cardiacatlas.org/motion-tracking-challenge/) were then analysed to obtain cardiac displacement fields and strain maps. The results were then compared with estimates from Harmonic Phase analysis (HARP) technique.

Results: For a known displacement field imposed on both the synthetic grid image and the phantom image, CIV is accurate for 3-pixel and larger displacements on a 512 &times; 512 image with (CB,SB)=(25,55) pixels. Further validation of our method is achieved by showing that our estimated landmark positions on patient images fall within the inter-observer variability in the ground truth. The effectiveness of our approach to analyse patient images is then established by calculating dense displacement fields throughout a cardiac cycle, and were found to be physiologically consistent. Circumferential strains were estimated at the apical, mid and basal slices of the heart, and were shown to compare favorably with those of HARP over the entire cardiac cycle, except in a few (&sim;4) of the segments in the 17-segment AHA model. The radial strains, however, are underestimated by our method in most segments when compared with HARP.

Conclusions: In summary, we have demonstrated the capability of CIV to accurately and efficiently quantify cardiac deformation from tMR images. Furthermore, physiologically consistent displacement fields and circumferential strain curves in most regions of the heart indicate that our approach, upon automating some pre-processing steps and testing in clinical trials, can potentially be implemented in a clinical setting.

Electronic supplementary material: The online version of this article (doi:10.1186/s12880-017-0195-7) contains supplementary material, which is available to authorized users.

Mentions:
The tMR images of the phantom (Fig. 1a) and patients (Fig.1c) used in this study are from the “Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges” (STACOM) challenge held during the 2011 edition of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Conference [23]. The 4D tMR sequence was obtained with three sequential breath hold acquisitions in each orthogonal direction (TR/TE = 7.0/3.2 ms, flip angle =19−25°, tag distance: 7 mm)[24]. Images were acquired with reduced field-of-view enclosing the left ventricle (LV; 108x108x108 mm3); the voxel dimension of each image is 112 × 112 × 111 pixels. The images are also supplemented by positions of landmark points tracked by two independent observers over an entire cardiac cycle, henceforth referred to as ground truth. The data is hosted by the Cardiac Atlas Project. It was acquired at the Division of Imaging Sciences and Biomedical Engineering, King’s College London, United Kingdom, and the Department of Internal Medicine II - Cardiology, University of Ulm, Germany, with ethics committee and patient approval. More details on the data can be found in [23].Fig. 1

Mentions:
The tMR images of the phantom (Fig. 1a) and patients (Fig.1c) used in this study are from the “Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges” (STACOM) challenge held during the 2011 edition of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Conference [23]. The 4D tMR sequence was obtained with three sequential breath hold acquisitions in each orthogonal direction (TR/TE = 7.0/3.2 ms, flip angle =19−25°, tag distance: 7 mm)[24]. Images were acquired with reduced field-of-view enclosing the left ventricle (LV; 108x108x108 mm3); the voxel dimension of each image is 112 × 112 × 111 pixels. The images are also supplemented by positions of landmark points tracked by two independent observers over an entire cardiac cycle, henceforth referred to as ground truth. The data is hosted by the Cardiac Atlas Project. It was acquired at the Division of Imaging Sciences and Biomedical Engineering, King’s College London, United Kingdom, and the Department of Internal Medicine II - Cardiology, University of Ulm, Germany, with ethics committee and patient approval. More details on the data can be found in [23].Fig. 1

Background: Tagged Magnetic Resonance (tMR) imaging is a powerful technique for determining cardiovascular abnormalities. One of the reasons for tMR not being used in routine clinical practice is the lack of easy-to-use tools for image analysis and strain mapping. In this paper, we introduce a novel interdisciplinary method based on correlation image velocimetry (CIV) to estimate cardiac deformation and strain maps from tMR images.

Methods: CIV, a cross-correlation based pattern matching algorithm, analyses a pair of images to obtain the displacement field at sub-pixel accuracy with any desired spatial resolution. This first time application of CIV to tMR image analysis is implemented using an existing open source Matlab-based software called UVMAT. The method, which requires two main input parameters namely correlation box size (CB) and search box size (SB), is first validated using a synthetic grid image with grid sizes representative of typical tMR images. Phantom and patient images obtained from a Medical Imaging grand challenge dataset (http://stacom.cardiacatlas.org/motion-tracking-challenge/) were then analysed to obtain cardiac displacement fields and strain maps. The results were then compared with estimates from Harmonic Phase analysis (HARP) technique.

Results: For a known displacement field imposed on both the synthetic grid image and the phantom image, CIV is accurate for 3-pixel and larger displacements on a 512 &times; 512 image with (CB,SB)=(25,55) pixels. Further validation of our method is achieved by showing that our estimated landmark positions on patient images fall within the inter-observer variability in the ground truth. The effectiveness of our approach to analyse patient images is then established by calculating dense displacement fields throughout a cardiac cycle, and were found to be physiologically consistent. Circumferential strains were estimated at the apical, mid and basal slices of the heart, and were shown to compare favorably with those of HARP over the entire cardiac cycle, except in a few (&sim;4) of the segments in the 17-segment AHA model. The radial strains, however, are underestimated by our method in most segments when compared with HARP.

Conclusions: In summary, we have demonstrated the capability of CIV to accurately and efficiently quantify cardiac deformation from tMR images. Furthermore, physiologically consistent displacement fields and circumferential strain curves in most regions of the heart indicate that our approach, upon automating some pre-processing steps and testing in clinical trials, can potentially be implemented in a clinical setting.

Electronic supplementary material: The online version of this article (doi:10.1186/s12880-017-0195-7) contains supplementary material, which is available to authorized users.